Machine Learning Infrastructure Engineer [UAE Based]

AI71; Greater London; On-site; GBP 60,000 - 100,000; Full time

Job summary

An innovative technology firm is seeking a skilled ML Infrastructure Senior Engineer to join their dynamic AI/ML platform team. This role focuses on deploying and optimizing large-scale machine learning systems, leveraging advanced inference engines. You will collaborate closely with research and data science teams to enhance model capabilities and manage the MLOps lifecycle. If you're passionate about machine learning and eager to work in a fast-paced environment, this opportunity offers a chance to make a significant impact in the field of AI.

Qualifications

• Experience deploying LLMs/SLMs using inference engines.
• Proficient in Python and C/C++ for optimization.
• Strong knowledge of MLOps and AWS services.

Responsibilities

• Deploy and optimize ML models using advanced inference engines.
• Collaborate with teams for model fine-tuning and automation.
• Manage ML model lifecycle on AWS or other cloud platforms.

Skills

Deployment of LLMs/SLMs

Fine-tuning language models

Understanding of LLM architecture

Python programming

C/C++ programming

AWS services for ML

MLOps lifecycle knowledge

Cloud infrastructure management

Tools

vLLM

Triton

Docker

Kubernetes

MLflow

SageMaker

EC2

EKS

Job description

Job Title: ML Infrastructure Senior Engineer

Location: Abu Dhabi, United Arab Emirates [Full relocation package provided]

Job Overview

We are seeking a skilled ML Infrastructure Engineer to join our growing AI/ML platform team. This role is ideal for someone passionate about large-scale machine learning systems and has hands-on experience deploying LLMs/SLMs using advanced inference engines like vLLM. You will play a critical role in designing, deploying, optimizing, and managing ML models and the infrastructure around them—both for inference, fine-tuning and continued pre-training.

Key Responsibilities

· Deploy large-scale or small language models (LLMs/SLMs) using inference engines (e.g., vLLM, Triton, etc.).

· Collaborate with research and data science teams to fine-tune models or build automated fine-tuning pipelines.

· Extend inference-level capabilities by integrating advanced features such as multi-modality, real-time inferencing, model quantization, and tool-calling.

· Evaluate and recommend optimal hardware configurations (GPU, CPU, RAM) based on model size and workload patterns.

· Build, test, and optimize LLMs Inference for consistent model deployment.

· Implement and maintain infrastructure-as-code to manage scalable, secure, and elastic cloud-based ML environments.

· Ensure seamless orchestration of the MLOps lifecycle, including experiment tracking, model registry, deployment automation, and monitoring.

· Manage ML model lifecycle on AWS (preferred) or other cloud platforms.

· Understand LLM architecture fundamentals to design efficient scalability strategies for both inference and fine-tuning processes.

Required Skills

Core Skills:

· Proven experience deploying LLMs or SLMs using inference engines like vLLM, TGI, or similar.

· Experience in fine-tuning language models or creating automated pipelines for model training and evaluation.

· Deep understanding of LLM architecture fundamentals (e.g., attention mechanisms, transformer layers) and how they influence infrastructure scalability and optimization.

· Strong understanding of hardware-resource alignment for ML inference and training.

Technical Proficiency:

· Programming experience in Python and C/C++, especially for inference optimization.

· Solid understanding of the end-to-end MLOps lifecycle and related tools.

· Experience with containerization, image building, and deployment (e.g., Docker, Kubernetes optional).

· Hands-on experience with AWS services for ML workloads (SageMaker, EC2, EKS, etc.) or equivalent services in Azure/GCP.

· Ability to manage cloud infrastructure to ensure high availability, scalability, and cost efficiency.

Nice-to-Have

· Experience with ML orchestration platforms like MLflow, SageMaker Pipelines, Kubeflow, or similar.

· Familiarity with model quantization, pruning, or other performance optimization techniques.

· Exposure to distributed training frameworks like Unsloth, DeepSpeed, Accelerate, or FSDP.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Information Technology

• Industries

  Technology, Information and Internet

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